牛津经济学词典 = Oxford Dictionary of Economics

Understanding and modeling the volatility measurements is important for forecasting the risk and for evaluating asset allocation decisions of stock market. The study have used the daily frequency data from January 1, 2002 to September 30, 2016 as an in-sample period to perform empirical analyses for modeling and predicting the volatility dynamics of Mexican stock market (IPC). To facilitate the variance forecast, the competing models are ARCH (p, q), GARCH (p, q), and its variations i.e. Glosten Jagnnathon Runkle GARCH, GARCH in Mean, Exponential GARCH, and Quadratic GARCH. The results of residual diagnostics suggested that stock market of Mexico is characterized by heteroskedasticity, multicolinearity, non-normality, and serial correlation. Volatility measurements by ARCH and GARCH signify that the current conditional variance of Mexico is determined by its past price behavior and previous day volatility. Today’s volatility does impact the current stock returns as indicated by GARCH-M. Results of EGARCH explained that any large size news produces high volatility as compared to small size news. Effects of bad news are greater on the volatility of the Mexican stock market than good news. GJR GARCH described the asymmetric behavior of returns and variance in the politically conflicted regime during 2006-2012. Moreover, QGARCH effect is not linear. Findings have the implications for individuals and corporate investors about retaining their risky stocks.

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Financial Forecasting by Autoregressive Conditional Heteroscedasticity (ARCH) Family: A Case of Mexico

This paper aims to measure the effect of the volatility on the daily closing price for the (Iraqi dinar against US dollar) from (21 July 2011) until (21 July 2021) using the models of asymmetric general autoregressive conditional heterogeneity (APGARCH and AVGARCH). The parameter estimated by Maximum Likelihood Estimation method and the error term assumed two distributional (General error distribution and Student’s t distribution), the results showed that the APGARCH(1,2) with error term distributed (Student’s t) distribution is the best model for the return series of the (IQ/USD) exchange rate to get the lowest value according to the information criteria for determining ranks (AIC, BIC) in addition to the presence of the asymmetric effect of the leverage, and this is evidence that negative shocks affect volatility more than positive shocks (the impact of the positive shocks is less than the impact of the negative shocks).

Using apgarch/avgarch models Gaussian and non-Gaussian for modeling volatility exchange rate

This study looked into the supremacy of GARCH modelling over ARIMA modelling of Nigerian export commodity price index series. Data was collected from the Central Bank database from January 2000 to December 2020 and was analyzed using EVIEWS statistical software. The time plot of Nigerian export commodity price index (figure 1) shows that the series changes over time indicating that there is clearly a secular trend in it. By the Augmented Dickey-Fuller Test in Table 1, the series is non-stationary with p = 0.8989 > 0.05. Therefore there was need for differencing (see figure 2). After the series was differenced, a stationarity test was ran for the differenced series. The Augmented Dickey Fuller test of Table 2 shows that the differenced series is stationary with p = 0.0000 < 0.05, indicating that the trend has been removed through differencing. The EVIEWS statistical software was used in estimating the model. The correlogram of Figure 3 has spikes at lag 1 for the Autocorrelation function as well as for the partial autocorrelation function suggesting an ARMA (1,1). Then ARMA (1,1) was estimated for the differenced series (table 3) which showed that the series does not follow an ARMA (1,1) as suspected, the coefficients of the parameters are both non-significant statistically. After the failure of the ARIMA (1,1,1) we fitted the ARIMA (1,1,0) and ARIMA (0,1,1) to the original data. ARIMA (1, 1, 0), ARIMA (0,1, 1) and GARCH (1,1) having met the required assumptions as the appropriate model was used to estimate its parameters. From the result obtained, it was reveal that by the AIC, SC and HQ terms, the GARCH (1,1) model when compared to other models has proven to be adequate model for the modelling of differenced Nigerian Export Commodity Price Index series.

The supremacy of GARCH modelling over ARIMA modelling of Nigerian export commodity price index series

Modelling of Nigeria's Crude Oil Production and Price Volatilities was the major focus of this paper. The paper investigated the stationarity of the multivariate time series positive definiteness property, and the results revealed the stationarity of the multivariate time series. Special classes of MARCH and MGARCH models were fitted to the crude oil price and production volatilities, and MARCH [p (3,1)] outperformed other models with the aid of model selection criteria. The research has established interaction and interdependence between the two macroeconomic variables and has also revealed bilateral causality between crude oil production and price. This further substantiates the fact that every regime of oil price shock is tantamount to high variability in production, which, in effect, causes a setback in the economic development of the affected country. Hence, this paper proposes proactive measures that can always guarantee stability in crude oil production whenever the country experiences instability in the oil price in the international market.

Modeling Nigeria Crude Oil Production and Price Volatility Using Multivariate Generalized Autoregressive Conditional Heteroscedasticity Models

DOI: 10.9734/ARJOM/2021/v17i330282 Editor(s): (1) Prof. Megan M. Khoshyaran, Economics Traffic Clinic ETC, France. (2) Danilo Costarelli, University of Perugia, Italy. (3) Dr. Nikolaos D. Bagis, Aristotle University of Thessaloniki, Greece. Reviewers: (1) Siwapong Dheera-aumpon, Kasetsart University, Thailand. (2) Pavel Levin, St. John’s University, USA. Complete Peer review History: http://www.sdiarticle4.com/review-history/68132

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Deebom Zorle Dum

Papers

Comparative Modelling of Price Volatility in Nigerian Crude Oil Markets Using Symmetric and Asymmetric GARCH Models